Advancing Peptide-Based Biorecognition Elements for Biosensors Using in-Silico Evolution.

Area of Science:

• Biomolecular Engineering
• Biosensor Technology
• Computational Biology

Background:

• Biological recognition elements (BREs) are crucial for biosensors, enabling the detection of biomarkers for health monitoring.
• Developing BREs with high sensitivity and selectivity for low-concentration biomarkers remains a significant challenge.
• Peptides are promising BREs, but their affinity and specificity need optimization for clinical applications.

Purpose of the Study:

• To computationally evolve higher-affinity peptide-based BREs for cardiac troponin I (cTnI) detection.
• To demonstrate an in-silico strategy for designing sensitive BREs for biomarker monitoring.
• To improve the limit of detection for cardiac event biomarkers.

Main Methods:

• Utilized an in-silico approach to computationally evolve a parental affinity peptide (P2).
• Focused on enhancing the binding affinity and selectivity of peptide BREs.
• Applied computational methods to optimize BREs for detecting the cardiac biomarker troponin I.

Main Results:

• The evolved P2 affinity peptide exhibited approximately 16-fold higher affinity compared to the parent BRE.
• Achieved a limit of detection of approximately 10 fM (0.23 pg/mL) for troponin I.
• Demonstrated the efficacy of the in-silico evolution method for creating high-affinity BREs.

Conclusions:

• The described in-silico approach successfully evolved a peptide BRE with significantly enhanced affinity and sensitivity for troponin I.
• This computational strategy offers a powerful tool for designing bespoke BREs for various biomarkers.
• The findings have broad implications for advancing biosensor technology in human health and performance monitoring.